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113 results on '"Sreenivasan, Aswathy"'

7. Empowering Rural Entrepreneurship in the Digital Age: A Systematic Exploration of Potential Tracks of Social Computing

Author

Alka, T. A., Sreenivasan, Aswathy, Suresh, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Senjyu, Tomonobu, editor, So–In, Chakchai, editor, and Joshi, Amit, editor

Published
2024
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8. Rural Women’s Entrepreneurship in the Digital Age

Author

Alka, T. A., Sreenivasan, Aswathy, Suresh, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Kartik, S., editor

Published
2024
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9. Green Construction Project Management: A Bibliometric Analysis

Author

Gunanandhini, T., Sivakumar, S., Sreenivasan, Aswathy, Suresh, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Xie, Juanying, editor, and Rathore, Vijay Singh, editor

Published
2024
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39. Artificial intelligence in supply chain management: bibliometric analysis and futuristic research directions

Author

Shivathmica, C., Anushree, R., Sreenivasan, Aswathy, and Suresh, M.

Abstract

Supply chain is the most important key factor in determining the business success and also in determining how well the business is functioning. To enhance the efficiency in supply chain AI has been implemented by many companies. Using AI they can accurately predict the inventories, precise location can be tracked for shipping products and it is reduces warehouse and manufacturing costs by accurately predicting the manufacturing of products based on the demands. Many researches have been done on how AI will transform the efficiency of supply chain. This paper provides systematic review of how AI is implemented in SCM. Biblioshiny is used for analysis and Scopus is used for getting the data. The findings of the study show that how companies and managers should make use of technological advancements to enhance their business in the fast-growing world.

Published
2024
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47. Lean Implementation in the Construction Industry.

Author

A. C., Vishnu, Rajan, Shriya, Sreenivasan, Aswathy, and Suresh, M.

Subjects
CONSTRUCTION industry, LEAN management, PROJECT management, INDUSTRIAL management, KNOWLEDGE management
Abstract

The paper aims to present an overview of previous research on the application of "lean" in the construction industry, covering key aspects of recent discoveries, their limits, and potential future research directions. It makes two contributions to the body of knowledge. It initially divides the literature to provide an overview of completed research endeavors. Second, it points out a gap in the existing body of research and suggests fresh avenues for investigation. The findings indicate that prior research has had difficulty developing a sound theoretical foundation for the subject. To support the proposed "theories," "notions," and "paradigms," more study is needed. In short, there is a big need for more research because there hasn't been a thorough evaluation of how lean has been used in the construction industry. [ABSTRACT FROM AUTHOR]

Published
2023

48. Machine Learning in the Banking Sector.

Author

M., Hiran Kumar, G., Megavarshini, Sreenivasan, Aswathy, and Suresh, M.

Subjects
BANKING industry, MACHINE learning, ARTIFICIAL intelligence, KNOWLEDGE management, INDUSTRIAL management
Abstract

The paper aims to present an overview of previous research on "machine learning" applications in banking, covering key aspects of recent discoveries, their limits, and potential future research directions. It makes two contributions to the body of knowledge. It initially divides the literature to provide an overview of completed research endeavors. Second, it points out a gap in the existing body of research and suggests fresh avenues for investigation. The findings indicate that prior research has had difficulty developing a sound theoretical foundation for the subject. To support the proposed "theories," "notions," and "paradigms," more study is needed. In short, there is a big need for more research because there hasn't been a thorough evaluation of how machine learning has been used in banking. [ABSTRACT FROM AUTHOR]

Published
2023

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